Short, considered positions on the parts that decide whether AI survives production — the integration, the verification, the governance. Written by the founders, drawn from work on real systems under real audit requirements.
A running set of views on operationalizing enterprise AI — measured, specific, and revised as the work teaches us more.
The demonstration is the easy part — an afternoon with an API and a clean dataset. The durable work is wiring AI into the systems of record a business actually runs on, with verification and audit trails that withstand scrutiny. That integration layer, not the model, is where enterprise programs succeed or stall.
Full analysis forthcoming.
Consequential AI output should route through a named human owner, with its supporting evidence attached, before it acts — not after something breaks. We treat that as an architectural decision built into the data flow, not a feature flag that can be switched off under deadline pressure. Every override is logged and feeds back in.
Full analysis forthcoming.
Not every problem warrants a custom model. The framework: buy the commodity, build only where the workflow is genuinely a differentiator, and integrate to connect the two. Custom AI earns its keep where the process is proprietary — and an off-the-shelf tool wins nearly everywhere else.
Full analysis forthcoming.
More perspectives publish as engagements allow — the founders write these between builds, not instead of them.
In a 30-minute conversation, the founders will assess where AI delivers measurable return in your operation — and what it takes to deliver it in production.